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          hfl109
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                查看 9 个帖子 - 16 到 24（总计 24 个）
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                <div class="bbp-reply-header" id="post-236815">
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                   2007年10月29日 下午1:04
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/8394/">
                    自变量之间存在严重共线性，请问用多元回归作出的标准回归系数和偏相关系数还有意义吗？
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                   3 楼
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                  <p>
                   17个自变量之间的相关系数都在0。8以上，只有少数几个为0。7何0.6之间，而且显著，vif值达几百，上千
                   <br/>
                   样本是一个时间序列的变化过程，采集有11个点，重复2次，17个自变量（17个化合物）
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                   2007年10月29日 上午10:16
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/7945/">
                    请教：什么分析软件可以做投影寻踪回归分析？谢谢
                   </a>
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                  <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/7945/#post-236810">
                   11 楼
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                   到期刊网上下巴
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                <div class="bbp-reply-header" id="post-234966">
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                   2007年9月28日 下午2:37
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/7945/">
                    请教：什么分析软件可以做投影寻踪回归分析？谢谢
                   </a>
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                  <p>
                   多谢提供的信息
                   <br/>
                   可喜，我没发现专业的。。。？
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                <div class="bbp-reply-header" id="post-234555">
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                   2007年9月23日 上午6:53
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/7918/">
                    请教专家这样的数据适合做什么分析？[回归问题]
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                   转化后，也不成正态分布
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                   2007年9月23日 上午6:51
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/7945/">
                    请教：什么分析软件可以做投影寻踪回归分析？谢谢
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                   算盘也强呀，当年的蘑菇弹可就是用它算出来的
                   <br/>
                   几位专家，能给点专业上的建议不？
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                   2007年9月20日 上午4:53
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/7945/">
                    请教：什么分析软件可以做投影寻踪回归分析？谢谢
                   </a>
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                  <p>
                   多谢楼上两位
                   <br/>
                   这个软件好像太专业了，现在用dps做出了结果，可不知道结果怎么看，怎么计算各因子的贡献，请问两位专家能不能帮忙指教一下？多谢
                   <br/>
                   数据:
                   <br/>
                   bootstrap抽样重复数3
                   <br/>
                   Lambda=1.00000
                   <br/>
                   Backfit    投影    权重SSR
                   <br/>
                   1    1    3.4031
                   <br/>
                   1    2    1.2629
                   <br/>
                   1    3    1.2139
                   <br/>
                   1    4    1.1805
                   <br/>
                   <br/>
                   变量    投影1    投影2    投影3    投影4
                   <br/>
                   x1    0.3501    0.8774    0.9796    0.9867
                   <br/>
                   x2    0.3427    0.2196    -0.0484    -0.0042
                   <br/>
                   x3    0.4133    -0.2374    -0.1858    -0.1507
                   <br/>
                   x4    0.7649    0.2266    -0.0596    -0.0537
                   <br/>
                   <br/>
                   投影寻踪模型统计量：
                   <br/>
                   参数个数    GCV score    Log likelihood
                   <br/>
                   64    0.0176    2.0799
                   <br/>
                   <br/>
                   Y    Yhat    pX1    f1    pX2    f2    pX3    f3    pX4    f4
                   <br/>
                   99.65    99.2845    0    99.2415    0    0.0299    0    0.0069    0    0.0063
                   <br/>
                   99.88    99.2845    0    99.2415    0    0.0299    0    0.0069    0    0.0063
                   <br/>
                   93.88    93.8864    147.4356    93.5354    369.5114    0.3516    412.5364    0.0005    415.5287    -0.001
                   <br/>
                   96.09    96.0819    119.9468    95.204    300.6174    0.7955    335.6205    0.069    338.0549    0.0134
                   <br/>
                   51.59    51.59    1236.6877    51.8604    2284.6035    -0.2706    2563.8997    0.0002    2605.3449    0
                   <br/>
                   49.42    49.42    1529.9009    49.3846    2908.9765    0.0394    3264.5327    -0.004    3314.9014    0
                   <br/>
                   92.64    92.6665    97.25    93.0221    232.2206    -0.282    257.9964    -0.0544    260.3532    -0.0191
                   <br/>
                   91.08    91.0513    81.6474    91.6362    195.967    -0.6033    218.764    0.0068    220.643    0.0116
                   <br/>
                   99.21    99.2845    0    99.2415    0    0.0299    0    0.0069    0    0.0063
                   <br/>
                   99.12    99.2845    0    99.2415    0    0.0299    0    0.0069    0    0.0063
                   <br/>
                   79.72    79.72    795.0305    79.7766    1950.0121    -0.0566    2179.7135    0.0001    2196.743    0
                   <br/>
                   81.96    81.96    1037.3315    82.0692    2557.6606    -0.1078    2848.1129    -0.0014    2870.7632    0
                   <br/>
                   81.76    81.76    1155.2351    81.4082    2830.1315    0.3466    3140.8677    0.0051    3167.3124    0.0001
                   <br/>
                   85.78    85.78    619.299    85.6203    1528.5806    0.1597    1706.4165    -0.0001    1719.5952    0
                   <br/>
                   34.08    34.08    2077.2623    34.089    3717.0073    -0.0092    4159.7192    0.0002    4232.7633    0
                   <br/>
                   31.94    31.94    2242.9339    31.9354    4258.9871    0.0046    4755.7288    0    4830.0166    0
                   <br/>
                   73.05    73.0501    302.2009    72.9863    693.2084    0.0624    756.4903    0.0014    764.185    0.0001
                   <br/>
                   78.95    78.9664    205.9266    79.5469    476.8841    -0.5641    521.3128    -0.0128    526.554    -0.0037
                   <br/>
                   99.21    99.1385    0.4912    99.1568    1.231    0.0046    1.3743    -0.0111    1.3843    -0.0118
                   <br/>
                   98.32    99.1211    0.55    99.1467    1.3784    0.0016    1.5389    -0.0133    1.5501    -0.014
                   <br/>
                   69.44    69.3672    328.6931    69.1605    379.3729    0.2143    416.6032    -0.0036    433.0159    -0.004
                   <br/>
                   69.35    69.4231    328.28    69.2452    381.2953    0.1876    418.6864    -0.0054    435.0297    -0.0043
                   <br/>
                   48.3    48.2823    404.9824    48.5575    535.7273    -0.2895    491.9117    0.0084    505.096    0.006
                   <br/>
                   44.87    44.8671    448.9795    44.9827    609.9838    -0.1043    567.2908    -0.0129    581.5339    0.0016
                   <br/>
                   <br/>
                   <br/>
                   根据前面结果建立bootstrap抽样:
                   <br/>
                   <br/>
                   Bootstrap=1
                   <br/>
                   变量    投影1    投影2    投影3    投影4
                   <br/>
                   x1    0.3656    0.2111    0.0077    0.9916
                   <br/>
                   x2    0.3829    -0.0105    -0.1621    -0.0406
                   <br/>
                   x3    0.4134    -0.907    -0.7388    -0.0839
                   <br/>
                   x4    0.7407    -0.3116    0.5172    -0.0888
                   <br/>
                   <br/>
                   Bootstrap=2
                   <br/>
                   变量    投影1    投影2    投影3    投影4
                   <br/>
                   x1    0.382    0.0141    0.8993    0.0129
                   <br/>
                   x2    0.4319    -0.2476    0.1932    -0.2514
                   <br/>
                   x3    0.4392    -0.9185    -0.2917    -0.8873
                   <br/>
                   x4    0.682    0.3007    0.2402    0.3074
                   <br/>
                   <br/>
                   Bootstrap=3
                   <br/>
                   变量    投影1    投影2    投影3    投影4
                   <br/>
                   x1    0.3679    0.9818    0.0122    0.9374
                   <br/>
                   x2    0.3793    -0.0173    -0.2794    0.1637
                   <br/>
                   x3    0.4085    -0.1582    -0.8128    -0.2525
                   <br/>
                   x4    0.742    -0.0857    0.3192    0.1388
                   <br/>
                   bootstrap抽样结果:
                   <br/>
                   bootstrap重复系数的均值
                   <br/>
                   变量    投影1    投影2    投影3    投影4
                   <br/>
                   1    0.3718    0.4023    0.3064    0.6473
                   <br/>
                   2    0.3981    -0.0918    -0.0828    -0.0428
                   <br/>
                   3    0.4203    -0.6612    -0.6144    -0.4079
                   <br/>
                   4    0.7215    -0.0322    0.3589    0.1191
                   <br/>
                   <br/>
                   bootstrap重复系数的SE
                   <br/>
                   变量    投影1    投影2    投影3    投影4
                   <br/>
                   <br/>
                   1    0.0072    0.4176    0.4192    0.4492
                   <br/>
                   2    0.024    0.1102    0.201    0.1695
                   <br/>
                   3    0.0135    0.3557    0.2302    0.3459
                   <br/>
                   4    0.028    0.2528    0.1165    0.1624
                   <br/>
                   bootstrap系数符号比原来估计有差异的次数
                   <br/>
                   变量    投影1    投影2    投影3    投影4
                   <br/>
                   x1    0    0    0    0
                   <br/>
                   x2    0    3    1    1
                   <br/>
                   x3    0    0    0    0
                   <br/>
                   x4    0    2    3    2
                   <br/>
                   Yhat    Yhat-1se    Yhat+1se    Bootstrap mean
                   <br/>
                   99.2845    99.1711    99.398    99.0436
                   <br/>
                   99.2845    99.1711    99.398    99.0436
                   <br/>
                   93.8864    93.1624    94.6105    94.3578
                   <br/>
                   96.0819    95.4544    96.7094    95.6023
                   <br/>
                   51.59    50.6507    52.5293    52.247
                   <br/>
                   49.42    48.6538    50.1862    49.4384
                   <br/>
                   92.6665    92.3089    93.0241    91.3628
                   <br/>
                   91.0513    90.7293    91.3733    91.2488
                   <br/>
                   99.2845    99.1711    99.398    99.0436
                   <br/>
                   99.2845    99.1711    99.398    99.0436
                   <br/>
                   79.72    78.1803    81.2597    79.7132
                   <br/>
                   81.96    81.8512    82.0688    81.4784
                   <br/>
                   81.76    80.8875    82.6325    80.7254
                   <br/>
                   85.78    85.3695    86.1905    85.5401
                   <br/>
                   34.08    33.6528    34.5072    34.2443
                   <br/>
                   31.94    30.8203    33.0597    32.3194
                   <br/>
                   73.0501    71.9517    74.1486    73.7285
                   <br/>
                   78.9664    78.6498    79.2829    78.8955
                   <br/>
                   99.1385    99.0046    99.2724    99.0136
                   <br/>
                   99.1211    98.9846    99.2576    99.01
                   <br/>
                   69.3672    68.1078    70.6266    69.2967
                   <br/>
                   69.4231    68.5815    70.2648    70.7998
                   <br/>
                   48.2823    47.716    48.8486    48.0912
                   <br/>
                   44.8671    44.4529    45.2812    44.4173
                   <br/>
                   <br/>
                   x1    x2    x3    x4    Px1    F1    Px2    F2    Px3    F3    Px4    F4    预测值
                  </p>
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                <div class="bbp-reply-header" id="post-234159">
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                  <span class="bbp-reply-post-date">
                   2007年9月19日 上午1:08
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/7918/">
                    请教专家这样的数据适合做什么分析？[回归问题]
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                   5 楼
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                  <blockquote class="d4pbbc-quote">
                   <p>
                    [b]引用第2楼[i]Statsfu[/i]于[i]2007-09-18 17:20[/i]发表的“”[/b]:
                    <br/>
                    make some transformation like inverse , log. usually they will improve the performance of regression. Or, you can try generalized linear model.
                   </p>
                  </blockquote>
                  <p>
                   谢谢！！！
                   <br/>
                   分析看看
                  </p>
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                <div class="bbp-reply-header" id="post-234158">
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                  <span class="bbp-reply-post-date">
                   2007年9月19日 上午1:03
                  </span>
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/7918/">
                    请教专家这样的数据适合做什么分析？[回归问题]
                   </a>
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                   4 楼
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                  <p>
                   非常感谢！
                   <br/>
                   因子分析是分析各因子之间的吧，分析是不是只能对前4列分析，分析出来的贡献不知道是不是可以说就是对因变量y的贡献？
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                <div class="bbp-reply-header" id="post-234097">
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                  <span class="bbp-reply-post-date">
                   2007年9月18日 上午7:08
                  </span>
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/7909/">
                    请教通径分析结果怎么看？
                   </a>
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                  <p>
                   多谢！用的DPS，作了4个因子，12个样本（重复2次，24），结果不一致，不知道是不是数据不是正态分布引起的？
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